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refactor: README (#9712) (#10168)
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* refactor: README
* refactor: Use new README in `setup.py`

Signed-off-by: Oliver Koenig <okoenig@nvidia.com>
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# **NVIDIA NeMo Framework**

## Latest News

<!-- markdownlint-disable -->
<details open>
<summary><b>Large Language Models and Multimodal Models</b></summary>
<details>
<summary>
<a href="https://docs.nvidia.com/nemo-framework/user-guide/latest/llms/llama/index.html#new-llama-3-1-support for more information/">
New Llama 3.1 Support
</a> (2024-07-23)
</summary>
The NeMo Framework now supports training and customizing the Llama 3.1 collection of LLMs from Meta.
<br><br>
</details>
<summary><b>Large Language Models and Multimodal</b></summary>
<details>
<summary>
<a href="https://aws.amazon.com/blogs/machine-learning/accelerate-your-generative-ai-distributed-training-workloads-with-the-nvidia-nemo-framework-on-amazon-eks/">
Accelerate your Generative AI Distributed Training Workloads with the NVIDIA NeMo Framework on Amazon EKS
</a> (2024-07-16)
</summary>
NVIDIA NeMo Framework now runs distributed training workloads on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. For step-by-step instructions on creating an EKS cluster and running distributed training workloads with NeMo, see the GitHub repository <a href="https://github.com/aws-samples/awsome-distributed-training/tree/main/3.test_cases/2.nemo-launcher/EKS/"> here.</a>
<br><br>
</details>
<details>
<summary>
<a href="https://developer.nvidia.com/blog/nvidia-nemo-accelerates-llm-innovation-with-hybrid-state-space-model-support/">
NVIDIA NeMo Accelerates LLM Innovation with Hybrid State Space Model Support
</a> (2024/06/17)
</summary>
NVIDIA NeMo and Megatron Core now support pre-training and fine-tuning of state space models (SSMs). NeMo also supports training models based on the Griffin architecture as described by Google DeepMind.
<br><br>
</details>
<details>
<summary>
<a href="https://huggingface.co/models?sort=trending&search=nvidia%2Fnemotron-4-340B">
NVIDIA releases 340B base, instruct, and reward models pretrained on a total of 9T tokens.
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The walkthrough includes detailed instructions on how to set up a Google Cloud Project and pre-train a GPT model using the NeMo Framework.
<br><br>
</details>
<details>
<summary>
<a href="https://blogs.nvidia.com/blog/bria-builds-responsible-generative-ai-using-nemo-picasso/">
Bria Builds Responsible Generative AI for Enterprises Using NVIDIA NeMo, Picasso
</a> (2024/03/06)
</summary>
Bria, a Tel Aviv startup at the forefront of visual generative AI for enterprises now leverages the NVIDIA NeMo Framework.
The Bria.ai platform uses reference implementations from the NeMo Multimodal collection, trained on NVIDIA Tensor Core GPUs, to enable high-throughput and low-latency image generation.
Bria has also adopted NVIDIA Picasso, a foundry for visual generative AI models, to run inference.
<br><br>
</details>
<details>
<summary>
<a href="https://developer.nvidia.com/blog/new-nvidia-nemo-framework-features-and-nvidia-h200-supercharge-llm-training-performance-and-versatility/">
New NVIDIA NeMo Framework Features and NVIDIA H200
</a> (2023/12/06)
</summary>
NVIDIA NeMo Framework now includes several optimizations and enhancements,
including:
1) Fully Sharded Data Parallelism (FSDP) to improve the efficiency of training large-scale AI models,
2) Mix of Experts (MoE)-based LLM architectures with expert parallelism for efficient LLM training at scale,
3) Reinforcement Learning from Human Feedback (RLHF) with TensorRT-LLM for inference stage acceleration, and
4) up to 4.2x speedups for Llama 2 pre-training on NVIDIA H200 Tensor Core GPUs.
<br><br>
<a href="https://developer.nvidia.com/blog/new-nvidia-nemo-framework-features-and-nvidia-h200-supercharge-llm-training-performance-and-versatility">
<img src="https://github.com/sbhavani/TransformerEngine/blob/main/docs/examples/H200-NeMo-performance.png" alt="H200-NeMo-performance" style="width: 600px;"></a>
<br><br>
</details>
<details>
<summary>
<a href="https://blogs.nvidia.com/blog/nemo-amazon-titan/">
NVIDIA now powers training for Amazon Titan Foundation models
</a> (2023/11/28)
</summary>
NVIDIA NeMo Framework now empowers the Amazon Titan foundation models (FM) with efficient training of large language models (LLMs).
The Titan FMs form the basis of Amazon’s generative AI service, Amazon Bedrock.
The NeMo Framework provides a versatile framework for building, customizing, and running LLMs.
<br><br>
</details>
</details>

<details open>
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information, please consult the README located at the [gh-pages-src
branch](https://github.com/NVIDIA/NeMo/tree/gh-pages-src#readme).

## Blogs

<!-- markdownlint-disable -->
<details open>
<summary><b>Large Language Models and Multimodal Models</b></summary>
<details>
<summary>
<a href="https://blogs.nvidia.com/blog/bria-builds-responsible-generative-ai-using-nemo-picasso/">
Bria Builds Responsible Generative AI for Enterprises Using NVIDIA NeMo, Picasso
</a> (2024/03/06)
</summary>
Bria, a Tel Aviv startup at the forefront of visual generative AI for enterprises now leverages the NVIDIA NeMo Framework.
The Bria.ai platform uses reference implementations from the NeMo Multimodal collection, trained on NVIDIA Tensor Core GPUs, to enable high-throughput and low-latency image generation.
Bria has also adopted NVIDIA Picasso, a foundry for visual generative AI models, to run inference.
<br><br>
</details>
<details>
<summary>
<a href="https://developer.nvidia.com/blog/new-nvidia-nemo-framework-features-and-nvidia-h200-supercharge-llm-training-performance-and-versatility/">
New NVIDIA NeMo Framework Features and NVIDIA H200
</a> (2023/12/06)
</summary>
NVIDIA NeMo Framework now includes several optimizations and enhancements,
including:
1) Fully Sharded Data Parallelism (FSDP) to improve the efficiency of training large-scale AI models,
2) Mix of Experts (MoE)-based LLM architectures with expert parallelism for efficient LLM training at scale,
3) Reinforcement Learning from Human Feedback (RLHF) with TensorRT-LLM for inference stage acceleration, and
4) up to 4.2x speedups for Llama 2 pre-training on NVIDIA H200 Tensor Core GPUs.
<br><br>
<a href="https://developer.nvidia.com/blog/new-nvidia-nemo-framework-features-and-nvidia-h200-supercharge-llm-training-performance-and-versatility">
<img src="https://github.com/sbhavani/TransformerEngine/blob/main/docs/examples/H200-NeMo-performance.png" alt="H200-NeMo-performance" style="width: 600px;"></a>
<br><br>
</details>
<details>
<summary>
<a href="https://blogs.nvidia.com/blog/nemo-amazon-titan/">
NVIDIA now powers training for Amazon Titan Foundation models
</a> (2023/11/28)
</summary>
NVIDIA NeMo Framework now empowers the Amazon Titan foundation models (FM) with efficient training of large language models (LLMs).
The Titan FMs form the basis of Amazon’s generative AI service, Amazon Bedrock.
The NeMo Framework provides a versatile framework for building, customizing, and running LLMs.
<br><br>
</details>
</details>
<!-- markdownlint-enable -->

## Licenses

- [NeMo GitHub Apache 2.0
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